content: /* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at
  
      http://www.apache.org/licenses/LICENSE-2.0
  
  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License.
  ==============================================================================*/
  
  package org.tensorflow.lite;
  
  import java.io.File;
  import java.nio.ByteBuffer;
  import java.nio.MappedByteBuffer;
  import java.util.ArrayList;
  import java.util.HashMap;
  import java.util.List;
  import java.util.Map;
  import org.checkerframework.checker.nullness.qual.NonNull;
  
  /**
   * Driver class to drive model inference with TensorFlow Lite.
   *
   * <p>A {@code Interpreter} encapsulates a pre-trained TensorFlow Lite model, in which operations
   * are executed for model inference.
   *
   * <p>For example, if a model takes only one input and returns only one output:
   *
   * <pre>{@code
   * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
   *   interpreter.run(input, output);
   * }
   * }</pre>
   *
   * <p>If a model takes multiple inputs or outputs:
   *
   * <pre>{@code
   * Object[] inputs = {input0, input1, ...};
   * Map<Integer, Object> map_of_indices_to_outputs = new HashMap<>();
   * FloatBuffer ith_output = FloatBuffer.allocateDirect(3 * 2 * 4);  // Float tensor, shape 3x2x4.
   * ith_output.order(ByteOrder.nativeOrder());
   * map_of_indices_to_outputs.put(i, ith_output);
   * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
   *   interpreter.runForMultipleInputsOutputs(inputs, map_of_indices_to_outputs);
   * }
   * }</pre>
   *
   * <p>If a model takes or produces string tensors:
   *
   * <pre>{@code
   * String[] input = {"foo", "bar"};  // Input tensor shape is [2].
   * String[] output = new String[3][2];  // Output tensor shape is [3, 2].
   * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
   *   interpreter.runForMultipleInputsOutputs(input, output);
   * }
   * }</pre>
   *
   * <p>Orders of inputs and outputs are determined when converting TensorFlow model to TensorFlowLite
   * model with Toco, as are the default shapes of the inputs.
   *
   * <p>When inputs are provided as (multi-dimensional) arrays, the corresponding input tensor(s) will
   * be implicitly resized according to that array's shape. When inputs are provided as {@link Buffer}
   * types, no implicit resizing is done; the caller must ensure that the {@link Buffer} byte size
   * either matches that of the corresponding tensor, or that they first resize the tensor via {@link
   * #resizeInput()}. Tensor shape and type information can be obtained via the {@link Tensor} class,
   * available via {@link #getInputTensor(int)} and {@link #getOutputTensor(int)}.
   *
   * <p><b>WARNING:</b>Instances of a {@code Interpreter} is <b>not</b> thread-safe. A {@code
   * Interpreter} owns resources that <b>must</b> be explicitly freed by invoking {@link #close()}
   *
   * <p>The TFLite library is built against NDK API 19. It may work for Android API levels below 19,
   * but is not guaranteed.
   */
  public final class Interpreter implements AutoCloseable {
  
    /** An options class for controlling runtime interpreter behavior. */
    public static class Options {
      public Options() {}
  
      /**
       * Sets the number of threads to be used for ops that support multi-threading. Defaults to a
       * platform-dependent value.
       */
      public Options setNumThreads(int numThreads) {
        this.numThreads = numThreads;
        return this;
      }
  
      /** Sets whether to use NN API (if available) for op execution. Defaults to false (disabled). */
      public Options setUseNNAPI(boolean useNNAPI) {
        this.useNNAPI = useNNAPI;
        return this;
      }
  
      /**
       * Sets whether to allow float16 precision for FP32 calculation when possible. Defaults to false
       * (disallow).
       *
       * <p>WARNING: This is an experimental API and subject to change.
       */
      public Options setAllowFp16PrecisionForFp32(boolean allow) {
        this.allowFp16PrecisionForFp32 = allow;
        return this;
      }
  
      /**
       * Adds a {@link Delegate} to be applied during interpreter creation.
       *
       * <p>WARNING: This is an experimental interface that is subject to change.
       */
      public Options addDelegate(Delegate delegate) {
        delegates.add(delegate);
        return this;
      }
  
      /**
       * Advanced: Set if buffer handle output is allowed.
       *
       * <p>When a {@link Delegate} supports hardware acceleration, the interpreter will make the data
       * of output tensors available in the CPU-allocated tensor buffers by default. If the client can
       * consume the buffer handle directly (e.g. reading output from OpenGL texture), it can set this
       * flag to false, avoiding the copy of data to the CPU buffer. The delegate documentation should
       * indicate whether this is supported and how it can be used.
       *
       * <p>WARNING: This is an experimental interface that is subject to change.
       */
      public Options setAllowBufferHandleOutput(boolean allow) {
        this.allowBufferHandleOutput = allow;
        return this;
      }
  
      int numThreads = -1;
      Boolean useNNAPI;
      Boolean allowFp16PrecisionForFp32;
      Boolean allowBufferHandleOutput;
      final List<Delegate> delegates = new ArrayList<>();
    }
  
    /**
     * Initializes a {@code Interpreter}
     *
     * @param modelFile: a File of a pre-trained TF Lite model.
     * @throws IllegalArgumentException if {@code modelFile} does not encode a valid TensorFlow Lite
     *     model.
     */
    public Interpreter(@NonNull File modelFile) {
      this(modelFile, /*options = */ null);
    }
  
    /**
     * Initializes a {@code Interpreter} and specifies the number of threads used for inference.
     *
     * @param modelFile: a file of a pre-trained TF Lite model
     * @param numThreads: number of threads to use for inference
     * @deprecated Prefer using the {@link #Interpreter(File,Options)} constructor. This method will
     *     be removed in a future release.
     */
    @Deprecated
    public Interpreter(@NonNull File modelFile, int numThreads) {
      this(modelFile, new Options().setNumThreads(numThreads));
    }
  
    /**
     * Initializes a {@code Interpreter} and specifies the number of threads used for inference.
     *
     * @param modelFile: a file of a pre-trained TF Lite model
     * @param options: a set of options for customizing interpreter behavior
     * @throws IllegalArgumentException if {@code modelFile} does not encode a valid TensorFlow Lite
     *     model.
     */
    public Interpreter(@NonNull File modelFile, Options options) {
      wrapper = new NativeInterpreterWrapper(modelFile.getAbsolutePath(), options);
    }
  
    /**
     * Initializes a {@code Interpreter} with a {@code ByteBuffer} of a model file.
     *
     * <p>The ByteBuffer should not be modified after the construction of a {@code Interpreter}. The
     * {@code ByteBuffer} can be either a {@code MappedByteBuffer} that memory-maps a model file, or a
     * direct {@code ByteBuffer} of nativeOrder() that contains the bytes content of a model.
     *
     * @throws IllegalArgumentException if {@code byteBuffer} is not a {@link MappedByteBuffer} nor a
     *     direct {@link Bytebuffer} of nativeOrder.
     */
    public Interpreter(@NonNull ByteBuffer byteBuffer) {
      this(byteBuffer, /* options= */ null);
    }
  
    /**
     * Initializes a {@code Interpreter} with a {@code ByteBuffer} of a model file and specifies the
     * number of threads used for inference.
     *
     * <p>The ByteBuffer should not be modified after the construction of a {@code Interpreter}. The
     * {@code ByteBuffer} can be either a {@code MappedByteBuffer} that memory-maps a model file, or a
     * direct {@code ByteBuffer} of nativeOrder() that contains the bytes content of a model.
     *
     * @deprecated Prefer using the {@link #Interpreter(ByteBuffer,Options)} constructor. This method
     *     will be removed in a future release.
     */
    @Deprecated
    public Interpreter(@NonNull ByteBuffer byteBuffer, int numThreads) {
      this(byteBuffer, new Options().setNumThreads(numThreads));
    }
  
    /**
     * Initializes a {@code Interpreter} with a {@code MappedByteBuffer} to the model file.
     *
     * <p>The {@code MappedByteBuffer} should remain unchanged after the construction of a {@code
     * Interpreter}.
     *
     * @deprecated Prefer using the {@link #Interpreter(ByteBuffer,Options)} constructor. This method
     *     will be removed in a future release.
     */
    @Deprecated
    public Interpreter(@NonNull MappedByteBuffer mappedByteBuffer) {
      this(mappedByteBuffer, /* options= */ null);
    }
  
    /**
     * Initializes a {@code Interpreter} with a {@code ByteBuffer} of a model file and a set of custom
     * {@link #Options}.
     *
     * <p>The ByteBuffer should not be modified after the construction of a {@code Interpreter}. The
     * {@code ByteBuffer} can be either a {@link MappedByteBuffer} that memory-maps a model file, or a
     * direct {@link ByteBuffer} of nativeOrder() that contains the bytes content of a model.
     *
     * @throws IllegalArgumentException if {@code byteBuffer} is not a {@link MappedByteBuffer} nor a
     *     direct {@link Bytebuffer} of nativeOrder.
     */
    public Interpreter(@NonNull ByteBuffer byteBuffer, Options options) {
      wrapper = new NativeInterpreterWrapper(byteBuffer, options);
    }
  
    /**
     * Runs model inference if the model takes only one input, and provides only one output.
     *
     * <p>Warning: The API is more efficient if a {@link Buffer} (preferably direct, but not required)
     * is used as the input/output data type. Please consider using {@link Buffer} to feed and fetch
     * primitive data for better performance. The following concrete {@link Buffer} types are
     * supported:
     *
     * <ul>
     *   <li>{@link ByteBuffer} - compatible with any underlying primitive Tensor type.
     *   <li>{@link FloatBuffer} - compatible with float Tensors.
     *   <li>{@link IntBuffer} - compatible with int32 Tensors.
     *   <li>{@link LongBuffer} - compatible with int64 Tensors.
     * </ul>
     *
     * @param input an array or multidimensional array, or a {@link Buffer} of primitive types
     *     including int, float, long, and byte. {@link Buffer} is the preferred way to pass large
     *     input data for primitive types, whereas string types require using the (multi-dimensional)
     *     array input path. When a {@link Buffer} is used, its content should remain unchanged until
     *     model inference is done, and the caller must ensure that the {@link Buffer} is at the
     *     appropriate read position. A {@code null} value is allowed only if the caller is using a
     *     {@link Delegate} that allows buffer handle interop, and such a buffer has been bound to the
     *     input {@link Tensor}.
     * @param output a multidimensional array of output data, or a {@link Buffer} of primitive types
     *     including int, float, long, and byte. When a {@link Buffer} is used, the caller must ensure
     *     that it is set the appropriate write position. A null value is allowed only if the caller
     *     is using a {@link Delegate} that allows buffer handle interop, and such a buffer has been
     *     bound to the output {@link Tensor}. See {@link Options#setAllowBufferHandleOutput()}.
     * @throws IllegalArgumentException if {@code input} or {@code output} is null or empty, or if
     *     error occurs when running the inference.
     */
    public void run(Object input, Object output) {
      Object[] inputs = {input};
      Map<Integer, Object> outputs = new HashMap<>();
      outputs.put(0, output);
      runForMultipleInputsOutputs(inputs, outputs);
    }
  
    /**
     * Runs model inference if the model takes multiple inputs, or returns multiple outputs.
     *
     * <p>Warning: The API is more efficient if {@link Buffer}s (preferably direct, but not required)
     * are used as the input/output data types. Please consider using {@link Buffer} to feed and fetch
     * primitive data for better performance. The following concrete {@link Buffer} types are
     * supported:
     *
     * <ul>
     *   <li>{@link ByteBuffer} - compatible with any underlying primitive Tensor type.
     *   <li>{@link FloatBuffer} - compatible with float Tensors.
     *   <li>{@link IntBuffer} - compatible with int32 Tensors.
     *   <li>{@link LongBuffer} - compatible with int64 Tensors.
     * </ul>
     *
     * <p>Note: {@code null} values for invididual elements of {@code inputs} and {@code outputs} is
     * allowed only if the caller is using a {@link Delegate} that allows buffer handle interop, and
     * such a buffer has been bound to the corresponding input or output {@link Tensor}(s).
     *
     * @param inputs an array of input data. The inputs should be in the same order as inputs of the
     *     model. Each input can be an array or multidimensional array, or a {@link Buffer} of
     *     primitive types including int, float, long, and byte. {@link Buffer} is the preferred way
     *     to pass large input data, whereas string types require using the (multi-dimensional) array
     *     input path. When {@link Buffer} is used, its content should remain unchanged until model
     *     inference is done, and the caller must ensure that the {@link Buffer} is at the appropriate
     *     read position.
     * @param outputs a map mapping output indices to multidimensional arrays of output data or {@link
     *     Buffer}s of primitive types including int, float, long, and byte. It only needs to keep
     *     entries for the outputs to be used. When a {@link Buffer} is used, the caller must ensure
     *     that it is set the appropriate write position.
     * @throws IllegalArgumentException if {@code inputs} or {@code outputs} is null or empty, or if
     *     error occurs when running the inference.
     */
    public void runForMultipleInputsOutputs(
        @NonNull Object[] inputs, @NonNull Map<Integer, Object> outputs) {
      checkNotClosed();
      wrapper.run(inputs, outputs);
    }
  
    /**
     * Expicitly updates allocations for all tensors, if necessary.
     *
     * <p>This will propagate shapes and memory allocations for all dependent tensors using the input
     * tensor shape(s) as given.
     *
     * <p>Note: This call is *purely optional*. Tensor allocation will occur automatically during
     * execution if any input tensors have been resized. This call is most useful in determining the
     * shapes for any output tensors before executing the graph, e.g.,
     * <pre>{@code
     * interpreter.resizeInput(0, new int[]{1, 4, 4, 3}));
     * interpreter.allocateTensors();
     * FloatBuffer input = FloatBuffer.allocate(interpreter.getInputTensor(0),numElements());
     * // Populate inputs...
     * FloatBuffer output = FloatBuffer.allocate(interpreter.getOutputTensor(0).numElements());
     * interpreter.run(input, output)
     * // Process outputs...
     * }</pre>
     *
     * @throws IllegalStateException if the graph's tensors could not be successfully allocated.
     */
    public void allocateTensors() {
      checkNotClosed();
      wrapper.allocateTensors();
    }
  
    /**
     * Resizes idx-th input of the native model to the given dims.
     *
     * @throws IllegalArgumentException if {@code idx} is negtive or is not smaller than the number of
     *     model inputs; or if error occurs when resizing the idx-th input.
     */
    public void resizeInput(int idx, @NonNull int[] dims) {
      checkNotClosed();
      wrapper.resizeInput(idx, dims, false);
    }
  
    /**
     * Resizes idx-th input of the native model to the given dims.
     *
     * <p>When `strict` is True, only unknown dimensions can be resized. Unknown dimensions are
     * indicated as `-1` in the array returned by `Tensor.shapeSignature()`.
     *
     * @throws IllegalArgumentException if {@code idx} is negtive or is not smaller than the number of
     *     model inputs; or if error occurs when resizing the idx-th input. Additionally, the error
     *     occurs when attempting to resize a tensor with fixed dimensions when `struct` is True.
     */
    public void resizeInput(int idx, @NonNull int[] dims, boolean strict) {
      checkNotClosed();
      wrapper.resizeInput(idx, dims, strict);
    }
  
    /** Gets the number of input tensors. */
    public int getInputTensorCount() {
      checkNotClosed();
      return wrapper.getInputTensorCount();
    }
  
    /**
     * Gets index of an input given the op name of the input.
     *
     * @throws IllegalArgumentException if {@code opName} does not match any input in the model used
     *     to initialize the {@link Interpreter}.
     */
    public int getInputIndex(String opName) {
      checkNotClosed();
      return wrapper.getInputIndex(opName);
    }
  
    /**
     * Gets the Tensor associated with the provdied input index.
     *
     * @throws IllegalArgumentException if {@code inputIndex} is negtive or is not smaller than the
     *     number of model inputs.
     */
    public Tensor getInputTensor(int inputIndex) {
      checkNotClosed();
      return wrapper.getInputTensor(inputIndex);
    }
  
    /** Gets the number of output Tensors. */
    public int getOutputTensorCount() {
      checkNotClosed();
      return wrapper.getOutputTensorCount();
    }
  
    /**
     * Gets index of an output given the op name of the output.
     *
     * @throws IllegalArgumentException if {@code opName} does not match any output in the model used
     *     to initialize the {@link Interpreter}.
     */
    public int getOutputIndex(String opName) {
      checkNotClosed();
      return wrapper.getOutputIndex(opName);
    }
  
    /**
     * Gets the Tensor associated with the provdied output index.
     *
     * <p>Note: Output tensor details (e.g., shape) may not be fully populated until after inference
     * is executed. If you need updated details *before* running inference (e.g., after resizing an
     * input tensor, which may invalidate output tensor shapes), use {@link #allocateTensors()} to
     * explicitly trigger allocation and shape propagation. Note that, for graphs with output shapes
     * that are dependent on input *values*, the output shape may not be fully determined until
     * running inference.
     *
     * @throws IllegalArgumentException if {@code outputIndex} is negtive or is not smaller than the
     *     number of model outputs.
     */
    public Tensor getOutputTensor(int outputIndex) {
      checkNotClosed();
      return wrapper.getOutputTensor(outputIndex);
    }
  
    /**
     * Returns native inference timing.
     *
     * @throws IllegalArgumentException if the model is not initialized by the {@link Interpreter}.
     */
    public Long getLastNativeInferenceDurationNanoseconds() {
      checkNotClosed();
      return wrapper.getLastNativeInferenceDurationNanoseconds();
    }
  
    /**
     * Turns on/off Android NNAPI for hardware acceleration when it is available.
     *
     * @deprecated Prefer using {@link Options#setUseNNAPI(boolean)} directly for enabling NN API.
     *     This method will be removed in a future release.
     */
    @Deprecated
    public void setUseNNAPI(boolean useNNAPI) {
      checkNotClosed();
      wrapper.setUseNNAPI(useNNAPI);
    }
  
    /**
     * Sets the number of threads to be used for ops that support multi-threading.
     *
     * @deprecated Prefer using {@link Options#setNumThreads(int)} directly for controlling thread
     *     multi-threading. This method will be removed in a future release.
     */
    @Deprecated
    public void setNumThreads(int numThreads) {
      checkNotClosed();
      wrapper.setNumThreads(numThreads);
    }
  
    /**
     * Advanced: Modifies the graph with the provided {@link Delegate}.
     *
     * <p>Note: The typical path for providing delegates is via {@link Options#addDelegate}, at
     * creation time. This path should only be used when a delegate might require coordinated
     * interaction between Interpeter creation and delegate application.
     *
     * <p>WARNING: This is an experimental API and subject to change.
     *
     * @throws IllegalArgumentException if error occurs when modifying graph with {@code delegate}.
     */
    public void modifyGraphWithDelegate(Delegate delegate) {
      checkNotClosed();
      wrapper.modifyGraphWithDelegate(delegate);
    }
  
    /**
     * Advanced: Resets all variable tensors to the default value.
     *
     * <p>If a variable tensor doesn't have an associated buffer, it will be reset to zero.
     *
     * <p>WARNING: This is an experimental API and subject to change.
     */
    public void resetVariableTensors() {
      checkNotClosed();
      wrapper.resetVariableTensors();
    }
  
    /** Release resources associated with the {@code Interpreter}. */
    @Override
    public void close() {
      if (wrapper != null) {
        wrapper.close();
        wrapper = null;
      }
    }
  
    @Override
    protected void finalize() throws Throwable {
      try {
        close();
      } finally {
        super.finalize();
      }
    }
  
    private void checkNotClosed() {
      if (wrapper == null) {
        throw new IllegalStateException("Internal error: The Interpreter has already been closed.");
      }
    }
  
    NativeInterpreterWrapper wrapper;
  }
  